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Title: Detection of Unusual Events and Trends in Complex Non-Stationary Data Streams

Abstract

The search for unusual events and trends hidden in multi-component, nonlinear, non-stationary, noisy signals is extremely important for a host of different applications, ranging from nuclear power plant and electric grid operation to internet traffic and implementation of non-proliferation protocols. In the context of this work, we define an unusual event as a local signal disturbance and a trend as a continuous carrier of information added to and different from the underlying baseline dynamics. The goal of this paper is to investigate the feasibility of detecting hidden intermittent events inside non-stationary signal data sets corrupted by high levels of noise, by using the Hilbert-Huang empirical mode decomposition method.

Authors:
 [1];  [1];  [1];  [2]
  1. ORNL
  2. University of Leeds, UK
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
Work for Others (WFO)
OSTI Identifier:
965821
DOE Contract Number:  
DE-AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: 5th American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation, Controls, and Human Machine Interface Technology, Albuquerque, NM, USA, 20061112, 20061116
Country of Publication:
United States
Language:
English
Subject:
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS; DETECTION; DISTURBANCES; IMPLEMENTATION; INTERNET; NUCLEAR POWER PLANTS; PROLIFERATION

Citation Formats

Perez, Rafael B, Protopopescu, Vladimir A, Worley, Brian Addison, and Perez, Cristina. Detection of Unusual Events and Trends in Complex Non-Stationary Data Streams. United States: N. p., 2006. Web.
Perez, Rafael B, Protopopescu, Vladimir A, Worley, Brian Addison, & Perez, Cristina. Detection of Unusual Events and Trends in Complex Non-Stationary Data Streams. United States.
Perez, Rafael B, Protopopescu, Vladimir A, Worley, Brian Addison, and Perez, Cristina. Sun . "Detection of Unusual Events and Trends in Complex Non-Stationary Data Streams". United States. doi:.
@article{osti_965821,
title = {Detection of Unusual Events and Trends in Complex Non-Stationary Data Streams},
author = {Perez, Rafael B and Protopopescu, Vladimir A and Worley, Brian Addison and Perez, Cristina},
abstractNote = {The search for unusual events and trends hidden in multi-component, nonlinear, non-stationary, noisy signals is extremely important for a host of different applications, ranging from nuclear power plant and electric grid operation to internet traffic and implementation of non-proliferation protocols. In the context of this work, we define an unusual event as a local signal disturbance and a trend as a continuous carrier of information added to and different from the underlying baseline dynamics. The goal of this paper is to investigate the feasibility of detecting hidden intermittent events inside non-stationary signal data sets corrupted by high levels of noise, by using the Hilbert-Huang empirical mode decomposition method.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sun Jan 01 00:00:00 EST 2006},
month = {Sun Jan 01 00:00:00 EST 2006}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

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